code: def setUpClass(cls):
      super().setUpClass()
  
      import torch.distributed as dist
  
      # not use a fixed port for stress test
      tot_retry = 5
      for retry_no in range(tot_retry):
          try:
              port = random.randint(10000, 60000)
              dist.init_process_group(
                  backend="nccl",
                  init_method=f"tcp://localhost:{port}",
                  world_size=1,
                  rank=0,
              )
              break
          except RuntimeError:
              if retry_no == tot_retry - 1:
                  raise
              else:
                  continue
  
imports: import copy
  import os
  import random
  import torch
  from torch import nn
  from torch._dynamo.test_case import run_tests, TestCase
  from torch._dynamo.utils import same
  from torch._inductor import config
  from torch.testing._internal.inductor_utils import HAS_CUDA
label_desc: Use image node_id 1 for calling the TestLayoutOptim obj's underlying member method code with example usage: obj.setUpClass(cls) without return types